Biometrics is the study of automated methods for uniquely recognizing humans based upon one or more intrinsic physical or behavioral traits. In information technology, biometric authentications refer to technologies that measure and analyze human physical characteristics for authentication purposes. Examples of physical characteristics include fingerprints, eye retinas and irises, facial patterns and hand measurements.
A leading concern of existing biometric systems is that individual features that identify humans from others can be easily missed due to the lack of accurate acquisition of the biometric data, or due to the deviation of operational conditions. Iris recognition has been seen as a low error, high success method of retrieving biometric data. However, iris scanning and image processing has been costly and time consuming. Fingerprinting, facial patterns and hand measurements have afforded cheaper, quicker solutions.
During the past few years, iris recognition has matured sufficiently to allow it to compete economically with other biometric methods. However, inconsistency of acquisition conditions of iris images has led to rejecting valid subjects or validating imposters, especially when the scan is done under uncontrolled environmental conditions.
In contrast, under controlled conditions, iris recognition has proven to be very effective. This is true because iris recognition systems rely on more distinct features than other biometric techniques such as facial patterns and hand measurements and therefore provides a reliable solution by offering a much more discriminating biometric data set.
Although prototype systems and techniques had been proposed in the early 1980s, it was not until research in the 1990s that autonomous iris recognition systems were developed. The concepts discovered in this research have since been implemented in field devices. The overall approach is based on the conversion of a raw iris image into a numerical code that can be easily manipulated. The robustness of this approach and the following alternative approaches rely heavily on accurate iris segmentation. Iris segmentation is the process of locating and isolating the iris from the other parts of the eye. Iris segmentation is essential to the system's use. Computing iris features requires a high quality segmentation process that focuses on the subject's iris and properly extracts its borders. Such an acquisition process is sensitive to the acquisition conditions and has proven to be a very challenging problem. Current systems try to maximize the segmentation accuracy by constraining the operation conditions. Constraints may be placed on the lighting levels, position of the scanned eye, and environmental temperature. These constraints can lead to a more accurate iris acquisition, but are not practical in all real time operations.
Significant progress has been made to mitigate this problem; however, these developments were mostly built around the original methodology, namely, circular/elliptical contour segmentation that has proven to be problematic under uncontrolled conditions. Other work introduces concepts which compete with the above discussed methodology, but still suffer similar issues with segmentation robustness under uncontrolled conditions.
Thus, it would be desirable to have a method that provides an iris recognition technique that is well suited for iris-at-a-distance applications, i.e. a system utilizing unconstrained conditions, which still provides an accurate, real-time result based on the collected biometric data.